Our team was involved in the design and implementation of MedNition’s machine learning framework, worked with diverse anonymized patient data and used a variety of medical information ontologies.
We developed an anomaly detection and machine learning based system for early detection of anomalies in the performance of Foursquare’s data infrastructure and systems.
We developed machine learning-based computer vision algorithms to facilitate the improvement of squash players’ technique and serve as a performance tracking solution.
We developed a new machine learning-based feature for Salmonsoft to automate the process of fish counting at fish viewing windows at fish ladders and weirs.
This project focused on the analysis of digital network traffic patterns and prediction of online network traffic flows.
Lunchcat is a demo chatbot our team created that uses natural language processing (NLP) and helps you and your friends easily and quickly split lunch costs.
We brainstormed an effective machine learning driven mobile app solution with Habitz and determined what kind of underlying ML technologies and smart behaviour systems to use and how to best apply them.
We provided strategic recommendations on how to effectively incorporate machine learning and a recommender system into the ActOn app and developed the final product.